In social media, data can be generated in the form of text, audio and video data. This data may be useful, for example, for service providers as the data may possess hidden information such as information about user satisfaction, user issues, popularity and statistical information about number of affected customers. Often this data is not analyzed, or when analyzed, may not reflect accurate sentiments conveyed in the data.
In currently existing solutions, for example, World Wide Web (web) or mobile applications, sentiment analysis may be performed using various natural language processing (NLP) techniques, which have sentiment word libraries or dictionary defined. These techniques have a static score that is automatically assigned to each word and combining the static scores of the words determines a sentiment for the whole sentence.